2024-09-10-baviera24a.md

title	booktitle	year	volume	series	month	publisher	pdf	url	abstract	layout	issn	id	tex_title	firstpage	lastpage	page	order	cycles	bibtex_editor	editor	bibtex_author	author	date	address	container-title	genre	issued	extras
Tailoring the Tails: Enhancing the Reliability of Probabilistic Load Forecasts	Proceedings of the Thirteenth Symposium on Conformal and Probabilistic Prediction with Applications	2024	230	Proceedings of Machine Learning Research	0	PMLR	https://raw.githubusercontent.com/mlresearch/v230/main/assets/baviera24a/baviera24a.pdf	https://proceedings.mlr.press/v230/baviera24a.html	Quantifying predictive uncertainty regarding future electricity demand is the main goal of probabilistic load forecasting. A good probabilistic model is often identified with forecasted densities that are as concentrated (“sharp”) as possible. However, this goal is frequently achieved by sacrificing forecast reliability, i.e. the statistical compatibility between forecasted densities and observed frequencies. In real-world applications, reliability is the crucial measure of model quality, especially when predicting distribution tails. We propose a new methodology for probabilistic load forecasting, introducing a novel loss function which allows an excellent balance between forecast sharpness and reliability. We apply the proposed modelling approach for predicting the electricity load on a benchmark dataset. Experimental results show that the obtained density forecasts are extremely reliable and also close to optimal in terms of sharpness and point accuracy.	inproceedings	2640-3498	baviera24a	Tailoring the Tails: Enhancing the Reliability of Probabilistic Load Forecasts	508	521	508-521	508	false	Vantini, Simone and Fontana, Matteo and Solari, Aldo and Bostr\"{o}m, Henrik and Carlsson, Lars	given family Simone Vantini given family Matteo Fontana given family Aldo Solari given family Henrik Boström given family Lars Carlsson	Baviera, Roberto and Manzoni, Pietro	given family Roberto Baviera given family Pietro Manzoni	2024-09-10		Proceedings of the Thirteenth Symposium on Conformal and Probabilistic Prediction with Applications	inproceedings	date-parts 2024 9 10